What do women undergoing <i>in vitro</i> fertilization (IVF) understand about their chance of IVF success?
Notice bibliographique
Résumé
STUDY QUESTION: How well informed are Australian women who undergo IVF about their chances of having a baby? SUMMARY ANSWER: Only one in four women estimated their individual chance of success with IVF accurately, with most women overestimating their chance. WHAT IS KNOWN ALREADY: Limited knowledge about infertility and infertility treatment in the general population is well-documented. The few studies that have investigated patients' knowledge about the chance of IVF success suggest that while IVF patients are aware of average success rates, they tend to be unrealistic about their own chance of success. STUDY DESIGN, SIZE, DURATION: We conducted an anonymous online survey of 217 women who had started IVF since 2018 in Australia. The survey was advertised on social media, enabling women from across Australia to participate. Responses were collected in June 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: The survey included questions on demographic characteristics and IVF history. It asked what participants thought their chance of having a baby from one IVF treatment cycle was, how they rated their knowledge about chance of success, and about their experience of receiving IVF-related information. Participants' estimations of their chance of success were compared with their chance as calculated by the Society for Assisted Reproductive Technology's (SART) online calculator. Responses to a free-text question about what information women wished they had been given when they started treatment were analysed thematically. MAIN RESULTS AND THE ROLE OF CHANCE: Only about a quarter (58/217, 27%) of participants accurately estimated their chance of having a baby within 20% relative to their SART calculated chance, with more than half (118/217, 54%) overestimating their chance. Ninety percent of women indicated that their preferred source of treatment information was a consultation with their doctor, despite less than half (44%) reporting that doctors explained the probability of having a baby with IVF well (mean 5.9/10). In free-text responses, many women also reported that they wished they had been given more realistic information about IVF and their chance of success. LIMITATIONS, REASONS FOR CAUTION: The dissemination method precludes calculation of response rate, and it is not possible to know if participants are representative of all women undergoing IVF. Additionally, we only surveyed women undergoing IVF, while those who decided not to have IVF were not included. Therefore, women who overestimated their chance may have been overrepresented. There is also inherent imprecision in the way understanding of chance of success was estimated. The potential impact of recall bias could neither be quantified nor excluded. It is difficult to determine to what extent women's lack of understanding of what is possible with IVF is due to poor information-provision by clinicians and the clinic, and how much can be explained by optimism bias. WIDER IMPLICATIONS OF THE FINDINGS: The finding of poor understanding of personal chance of success amongst women undergoing IVF in Australia requires further investigation to determine potential reasons for this. The findings can be used by clinics to develop strategies for improvement in the information-provision process to ensure that women can make informed decisions about their fertility treatment. STUDY FUNDING/COMPETING INTEREST(S): This study received no external funding. S.L. is supported by a NHMRC Investigator Grant (APP1195189). R.W. is supported by a NHMRC Investigator Grant (APP2009767). B.W.M. is supported by a NHMRC Investigator Grant (GNT1176437). B.W.M. reports consultancy for Merck and ObsEva and has received research funding and travel funding from Merck. The other authors have no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».